Preprint arXiv:2311.18717 [econ.GN]; last accessed January 4, 2024.
ISSN/ISBN: Not available at this time. DOI: Not available at this time.
Abstract: Recent studies using indirect statistical methods estimate that around 70% of traded value on centralized crypto exchanges like Binance, can be characterized as wash trading. This paper turns to NFT markets, where transaction transparency, including analysis of roundtrip trades and common wallet activities, allows for more accurate direct estimation methods to be applied. We find roughly 30% of NFT volume and between 45-95% of traded value, involve wash trading. More importantly, our approach enables a critical evaluation of common indirect estimation methods used in the literature. We find major differences in their effectiveness; some failing entirely. Roundedness filters, like those used in Cong et al. (2023), emerge as the most accurate. In fact, the two approaches can be closely aligned via hyper-parameter optimization if direct data is available.
Bibtex:
@misc{,
title={Crypto Wash Trading: Direct vs. Indirect Estimation},
author={Brett Hemenway Falk and Gerry Tsoukalas and Niuniu Zhang},
year={2023},
eprint={2311.18717},
archivePrefix={arXiv},
primaryClass={econ.GN}
}
Reference Type: Preprint
Subject Area(s): Accounting, Economics